As the computer industry retools to leverage massively parallel graphics processing units
(GPUs), this book is designed to meet the needs of working software developers who need …

GPUs are prevalent in modern computing systems at all scales. They consume a significant
fraction of the energy in these systems. However, vendors do not publish the actual cost of …

The use of the general dense matrix-matrix multiplication (GEMM) is fundamental for
obtaining high performance in many scientific computing applications. GEMMs for small …

The emergence of power efficiency as a primary constraint in processor and system design
poses new challenges concerning power and energy awareness for numerical libraries and …

General-purpose GPUs have been widely utilized to accelerate parallel applications. Given
a relatively complex programming model and fast architecture evolution, producing efficient …

Performance efficiency and scalability are the major design goals for high performance
computing (HPC) applications. However, it is challenging to achieve high efficiency and …

Transition to hybrid CPU/GPU platforms in high performance computing is challenging in the
aspect of efficient utilisation of the heterogeneous hardware and existing optimised software …

This paper describes extensions to Rice University's HPCToolkit performance tools to
support measurement and analysis of GPU-accelerated applications. To help developers …

Optimizing the performance of GPU kernels is challenging for both human programmers and
code generators. For example, CUDA programmers must set thread and block parameters …

To address the challenge of performance analysis on the US DOE's forthcoming exascale
supercomputers, Rice University has been extending its HPCToolkit performance tools to …